The Efficiency Hub has 531 inefficiencies, and its AI entries all cover cloud and API services: Bedrock, Azure OpenAI, the Claude and OpenAI APIs, Vertex AI. There is not one entry for the waste TokenShift exists to find: coding agents on employee workstations. Meanwhile Labs has already measured some of it (token compression that cut tool output 38.4% but raised billed cost 6.8%), and the TokenShift page describes others, such as restarting context with a cold cache. The open, free library PointFive built for cloud waste does not yet speak for the new product.
First action
List the ten coding-agent waste patterns TokenShift detects most often and draft the first three entries.
How to measure it
Monthly visits to AI-agent Hub entries
- Now
- –
- Target
- 5,000
- By
- 90 days
Steps
Pick patterns
Ten most frequent TokenShift detections.
Senior Product Manager, TokenShift 2 days
Pull evidence
Labs benchmark figures for each pattern.
Co-Heads of PointFive Labs 1 week
Write entries
Hub format: waste, billing model, detection, fix.
Senior Product Manager, TokenShift 1 week
Add the category
An "AI agents" provider and filter in the Hub.
Web team 3 days
Link to the product
Each entry points to the TokenShift detection.
Product Marketing 2 days
Open contributions
Invite practitioners to add patterns.
Creative Lead ongoing
Measure
Search impressions and demo requests from entries.
Integrated Marketing Lead monthly
Why it works
Add an "AI agents" provider to the Hub with 15 to 20 entries written in the Hub's format (the waste, the billing model behind it, how to detect it, how to fix it): cold-cache context restarts, retry loops after tool failures, frontier models on trivial tasks, oversized tool output, unused MCP servers loaded into every session, idle seats. Base each on Labs data or anonymized TokenShift metadata, link each to the matching TokenShift detection, and invite practitioners to contribute as the Hub's principles already promise.
Engineers search for the waste they see, not for a product name. The Hub already earns that search for cloud; extending it gives TokenShift a library of pages that answer coding-agent cost questions, a reference for the Creative Lead to build on, and proof of depth that gateways cannot show.
Ready to use
ChecklistFirst AI-agent Hub entries
[ ] Context restarted with a cold cache [ ] Retry loops after failed tool calls [ ] Frontier model used for low-complexity tasks [ ] Tool output larger than the task needs [ ] MCP servers loaded but never called [ ] Seats with no sessions in 30 days [ ] Each entry: waste, billing model, detection, fix, Labs source
Risks and how to handle them
Entries read as product pitches
Keep the Hub format neutral; put the product link at the end.
Patterns change with each agent release
Date every entry and review it quarterly.
Evidence
- Your websiteCloud Efficiency Hub (checked October 22, 2026): 531 inefficiencies and 166 service pages. Its AI entries cover cloud and API services (Amazon Bedrock, Azure OpenAI, the Claude and OpenAI APIs, Vertex AI, SageMaker, Databricks Model Serving), such as missing prompt caching and batch processing; there are no entries for AI coding agents on employee workstations (Claude Code, Cursor, Codex, Copilot). The About page promises the Hub "will expand to remain the definitive catalog of inefficiencies" as "new services and usage-based pricing models emerge", and that it is "Always open, always free" and "Community-powered".Source
- Your websitePointFive Labs: two papers. "Token reduction is not cost reduction" (July 2026): in the tested configuration "delivered tool-output tokens fell 38.4% while pooled billed cost rose 6.8%". "Prompt-induced waste in coding agents" (August 2026). The open AI Efficiency Benchmark publishes task manifests, retained data and analysis scripts that "can be re-analyzed without running a new paid campaign".Source
- Your websiteTokenShift page: "Control AI agents where they run"; supports Codex, Claude Code, GitHub Copilot CLI, Cursor, Devin Desktop and OpenCode, deployed through Jamf, Intune or JumpCloud; "A gateway sees the request. TokenShift sees the session, the code and the tools behind it"; coaching example: "you restart context every session even though your cache is cold"; budgets that "warn, block, or require approval"; prompts "Never sent"; "Up to 30 days of history from day one of install."Source
- HiringSenior Product Manager (Tel Aviv, posted October 5, 2026) to "own TokenShift, PointFive's product for finding and cutting wasted AI token spend" and "take TokenShift from early adoption to a core pillar of PointFive's platform"; the role works with "go-to-market teams on positioning, messaging, and launch plans" and tracks "adoption, cost savings, detection accuracy". The landscape to watch: "coding agents and harnesses, LLM gateways and routers, AI observability".Source
- HiringCreative Lead (New York, posted October 2, 2026; a second post in Tel Aviv, September 27): "The goal is buzz, shares, followers, mindshare and category authority. Pipeline follows attention, but attention is the job." Briefs include "Turn PointFive's research into stories that engineers, executives and press repeat", "Build something interactive, a tool, a game or a quiz, that people send to their team" and "Our category all looks and sounds the same." Success: "PointFive becomes the name people bring up first when the topic is infrastructure cost and AI cost." It is the first such hire: "no playbook, no asset library and no creative team underneath you."Source